People often speculate about their personal finances: *"Has my spending on groceries increased compared to past year?"*, *"How does my spending on clothes look for the past 6 months?"*. However, answering them with cloud-hosted LLMs transmits sensitive financial data to external servers, exposing users to data leakage and inferential privacy risks. We present **SpendSight**, a study of fully on-device personal finance Q A tool calling that keeps all data strictly local. We evaluate seven quantized models (six sub-2B and one 3B) across 115 test cases and five analytical tools, comparing single-agent against dual-agent (router-specialist) architectures. Dual-agent decomposition yields the largest time gains for the Qwen3.5 family. **Qwen3.5-0.8B** achieves **61.7% Correct Ratio** (**89.1% Task Accuracy**) in **3.0 s** with **1.2 GB RAM** in dual-agent mode. A 3B reference model (**G9v3-3B**, Q4_K_M) achieves **71.7% Correct Ratio** (**90.6% Task Accuracy**) at **2.3 s** in single agent mode, suggesting that larger models with aggressive quantization can bypass decomposition.